Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	132,772
2	South Dakota	129,757
3	Rhode Island	124,367
4	Utah	118,242
5	Arizona	114,564
6	Tennessee	114,199
7	Oklahoma	109,433
8	Iowa	108,983
9	Arkansas	108,508
10	Wisconsin	107,607
11	Nebraska	106,309
12	South Carolina	103,883
13	Alabama	103,752
14	Kansas	103,377
15	Mississippi	101,221
16	Indiana	100,531
17	Idaho	98,559
18	Nevada	97,360
19	Illinois	95,940
20	Montana	95,874
21	Wyoming	95,639
22	Georgia	95,254
23	New Jersey	95,084
24	Texas	94,434
25	Louisiana	94,337
26	Kentucky	94,207
27	Missouri	93,780
28	Delaware	93,196
29	Florida	92,394
30	California	91,816
31	New York	90,237
32	New Mexico	89,975
33	Minnesota	88,479
34	Massachusetts	87,728
35	North Carolina	85,102
36	Ohio	84,884
37	Connecticut	82,449
38	Alaska	82,396
39	Colorado	77,647
40	Pennsylvania	76,228
41	West Virginia	75,897
42	Virginia	69,959
43	Michigan	67,735
44	Maryland	65,319
45	District of Columbia	60,545
46	New Hampshire	58,152
47	Washington	46,478
48	Puerto Rico	42,863
49	Oregon	37,954
50	Maine	35,253
51	Vermont	27,319
52	Hawaii	20,027

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	392
2	New Jersey	369
3	Alabama	367
4	New York	351
5	Connecticut	315
6	Michigan	225
7	Alaska	219
8	Montana	208
9	Delaware	203
10	Tennessee	203
11	Massachusetts	195
12	Vermont	186
13	New Hampshire	184
14	Pennsylvania	176
15	Florida	175
16	West Virginia	161
17	North Carolina	157
18	Idaho	154
19	Texas	152
20	South Carolina	150
21	Minnesota	149
22	Louisiana	148
23	District of Columbia	140
24	Virginia	139
25	Maine	131
26	Nebraska	130
27	Kentucky	126
28	Iowa	121
29	Maryland	117
30	Colorado	116
31	Utah	114
32	Georgia	113
33	Illinois	113
34	South Dakota	112
35	Ohio	111
36	Wyoming	108
37	North Dakota	90
38	Oklahoma	87
39	Wisconsin	85
40	Indiana	82
41	Arizona	81
42	Nevada	79
43	New Mexico	79
44	Washington	79
45	Mississippi	75
46	Arkansas	71
47	Missouri	64
48	California	61
49	Kansas	53
50	Oregon	51
51	Puerto Rico	49
52	Hawaii	32

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,698
2	New York	2,499
3	Rhode Island	2,438
4	Massachusetts	2,421
5	Mississippi	2,328
6	Arizona	2,277
7	Connecticut	2,187
8	South Dakota	2,161
9	Louisiana	2,134
10	Alabama	2,108
11	North Dakota	1,953
12	Pennsylvania	1,930
13	Indiana	1,912
14	New Mexico	1,841
15	Illinois	1,833
16	Arkansas	1,820
17	Iowa	1,793
18	South Carolina	1,724
19	Tennessee	1,690
20	Michigan	1,681
21	Georgia	1,670
22	Nevada	1,667
23	Kansas	1,659
24	Texas	1,614
25	Delaware	1,554
26	Ohio	1,539
27	Florida	1,510
28	District of Columbia	1,476
29	California	1,435
30	Missouri	1,433
31	West Virginia	1,420
32	Maryland	1,336
33	Montana	1,307
34	Wisconsin	1,233
35	Oklahoma	1,210
36	Minnesota	1,208
37	Wyoming	1,197
38	Virginia	1,183
39	Kentucky	1,156
40	Nebraska	1,156
41	North Carolina	1,120
42	Idaho	1,080
43	Colorado	1,064
44	New Hampshire	884
45	Washington	683
46	Puerto Rico	652
47	Utah	633
48	Oregon	556
49	Maine	539
50	Alaska	399
51	Vermont	344
52	Hawaii	316

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Oklahoma	7
2	Rhode Island	5
3	West Virginia	5
4	Arkansas	4
5	Louisiana	4
6	New York	4
7	Virginia	4
8	California	3
9	Connecticut	3
10	Delaware	3
11	Florida	3
12	Idaho	3
13	Kentucky	3
14	Massachusetts	3
15	Mississippi	3
16	Ohio	3
17	Pennsylvania	3
18	Texas	3
19	Georgia	2
20	Iowa	2
21	Maryland	2
22	New Jersey	2
23	Arizona	1
24	Illinois	1
25	Indiana	1
26	Kansas	1
27	Michigan	1
28	Missouri	1
29	Montana	1
30	Nevada	1
31	New Mexico	1
32	North Carolina	1
33	Oregon	1
34	South Dakota	1
35	Utah	1
36	Washington	1
37	Wyoming	1
38	Alabama	0
39	Alaska	0
40	Colorado	0
41	District of Columbia	0
42	Hawaii	0
43	Maine	0
44	Minnesota	0
45	Nebraska	0
46	New Hampshire	0
47	North Dakota	0
48	Puerto Rico	0
49	South Carolina	0
50	Tennessee	0
51	Vermont	0
52	Wisconsin	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	346,807	1	99
Chattahoochee	Georgia	303,383	2	99
Bent	Colorado	265,914	3	99
Dewey	South Dakota	243,551	4	99
Lincoln	Arkansas	243,474	5	99
Davidson	Tennessee	132,608	222	92
Richland	South Carolina	102,668	1057	66
York	South Carolina	98,498	1252	60
Orange	California	83,142	1975	37
Pierce	Washington	44,397	2905	7

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	8,346	1	99
Jerauld	South Dakota	7,948	2	99
Foard	Texas	7,792	3	99
Galax city	Virginia	7,720	4	99
Emporia city	Virginia	7,669	5	99
Orange	California	1,413	1817	42
Davidson	Tennessee	1,256	2008	36
Richland	South Carolina	1,244	2027	35
York	South Carolina	1,214	2072	34
Pierce	Washington	649	2710	13

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.